An evaluation study on text categorization using automatically generated labeled dataset
Zhu, D. and Wong, K.W. (2017) An evaluation study on text categorization using automatically generated labeled dataset. Neurocomputing, 249 . pp. 321-336.
*Subscription may be required
Abstract
Naïve Bayes, k-nearest neighbors, Adaboost, support vector machines and neural networks are five among others commonly used text classifiers. Evaluation of these classifiers involves a variety of factors to be considered including benchmark used, feature selections, parameter settings of algorithms, and the measurement criteria employed. Researchers have demonstrated that some algorithms outperform others on some corpus, however, inconsistency of human labeling and high dimensionality of feature spaces are two issues to be addressed in text categorization. This paper focuses on evaluating the five commonly used text classifiers by using an automatically generated text document collection which is labeled by a group of experts to alleviate subjectivity of human category assignments, and at the same time to examine the influence of the number of features on the performance of the algorithms.
Publication Type: | Journal Article |
---|---|
Murdoch Affiliation: | School of Engineering and Information Technology |
Publisher: | Elsevier B.V. |
Copyright: | © 2017 Elsevier B.V. |
URI: | http://researchrepository.murdoch.edu.au/id/eprint/36380 |
![]() |
Item Control Page |
Downloads
Downloads per month over past year